Application of machine learning algorithms to predict hotel occupancy

Kozlovskis, Konstantins; Liu, Yuanyuan; Lace, Natalja; Meng, Yun Article Application of machine learning algorithms to predict hotel occupancy Journal of Business Economics and Management (JBEM) Provided in Cooperation with: Vilnius Gediminas Technical University (VILNIUS TECH) Suggested Citation: Kozlovskis, Konstantins; Liu, Yuanyuan; Lace, Natalja; Meng, Yun (2023) : Application of machine learning algorithms to predict hotel occupancy, Journal of Business Economics and Management (JBEM), ISSN 2029-4433, Vilnius Gediminas Technical University, Vilnius, Vol. 24, Iss. 3, pp. 594-613, https://doi.org/10.3846/jbem.2023.19775 This Version is available at: https://hdl.handle.net/10419/317640 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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Published by Vilnius Gediminas Technical University This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. *Corresponding author. E-mail: guizhouliuyuany[email protected]om Journal of Business Economics and Management ISSN 1611-1699 / eISSN 2029-4433 2023 Volume 24 Issue 3: 594–613 https://doi.org/10.3846/jbem.2023.19775 APPLICATION OF MACHINE LEARNING ALGORITHMS TO PREDICT HOTEL OCCUPANCY Konstantins KOZLOVSKIS 1, Yuanyuan LIU 1*, Natalja LACE 1, Yun MENG 2 1Department of Corporate Finance and Economics, Faculty of Engineering Economics and Management, Riga Technical University, Riga, Latvia 2School of Management, Guizhou University, Guizhou, China Received 28 June 2023; accepted 17 August 2023 Abstract. The development and availability of information technology and the possibility of deep integration of internal IT systems with external ones gives a powerful opportunity to analyze data online based on external data providers. Recently, machine learning algorithms play a significant role in predicting different processes. This research aims to apply several machine learning algorithms to predict high frequent daily hotel occupancy at a Chinese hotel. Five machine learning models (bagged CART, bagged MARS, XGBoost, random forest, SVM) were optimized and applied for predicting occupancy. All models are compared using different model accuracy measures and with an ARDL model chosen as a benchmark for comparison. It was found that the bagged CART model showed the most relevant results (R2 > 0.50) in all periods, but the model could not beat the traditional ARDL model. Thus, despite the original use of machine learning algorithms in solving regression tasks, the models used in this research could have been more effective than the benchmark model. In addition, the variables’ importance was used to check the hypothesis that the Baidu search index and its components can be used in machine learning models to predict hotel occupancy. Keywords: bagged CART, bagged MARS, XGBoost, random forest, SVM, hotel occupancy. JEL Classification: L10, L80, C10, C40. Introduction Machine learning and data-driven methods have been widely used in research and practice of forecasting (Strielkowski etal., 2023). As a scientific technology of how computers mimic human learning behavior (Al Shehhi & Karathanasopoulos, 2020), it acquires new knowledge or experience and reorganizes existing knowledge structures to improve performance. Machine learning could learn the laws and patterns of massive data through computers, mining poten- Journal of Business Economics and Management, 2023, 24(3): 594–613 595 tial information, and is widely used to solve problems such as classification, regression, and clustering (Ahani etal., 2019; Aryai & Glodsworthy, 2023; Divasón etal., 2023; Kaya etal., 2022; Viverit etal., 2023). It is related to data learning that could help machines learn patterns from existing complex data, and is therefore widely used to forecast future behavioral outcomes and trends (Boriratrit etal., 2023; Gong etal., 2023; Kamm etal., 2023; Khalil etal., 2022; Kolomoyets & Dickinger, 2023; Sayed etal., 2023). Many types of machine learning methods have been published in research for decades. Multiple classification methods can be based on the emphasis on different learning strategies, such as (1) machine learning that simulates the human brain and (2) directly adopting mathematical methods. The mathematical methods mainly consist of three elements: statistical machine learning, model, strategy, and algorithm. A time series is a classic trend prediction method that assumes future values composed of patterns of current and historical data, and it is applied to constructing a model from historical data and then predict future data in machine learning (Caicedo-Torres & Payares, 2016; Mehmood etal., 2022; Qin etal., 2023; Sun & Lu, 2023). According to Calero- Sanz etal. (2022), Jiang etal. (2023), and Yang etal. (2015), the tourism sector, especially the hotel industry, has adopted machine learning for forecasting room booking and cancellation, demand, prices, and occupancy. This trend is approved by Huang and Zheng (2023), Li etal. (2020), Sánchez etal. (2020), Sánchez-Medina and Sánchez (2020), Koupriouchina etal. (2014), Al Shehhi and Karathanasopoulos (2020), and Zhai etal. (2023). However, the hotel’s corresponding forecasting accuracy has received widespread attention from scholars and industries. Hotel occupancy is the ratio of the total number of rooms a hotel occupies to the total number of rooms, usually expressed as a percentage. The hotel occupancy rate is one of the essential indicators to measure the hotel’s operational status and management level, directly affecting the hotel’s revenue and profits. Various factors, such as seasonal factors, pricing strategies, market demand, etc., influence hotel occupancy rates. In order to improve hotel occupancy, hotel managers can adopt various strategies, such as regular market analysis and competitive intelligence collection, formulating reasonable price strategies and promotion strategies, and improving customer satisfaction and loyalty. A hotel occupancy rate of over 60% is considered ideal, but different types of hotels and market demand can also impact the occupancy standards. Therefore, hotel occupancy prediction is an essential managerial tool for hotels. Hotel occupancy prediction could use data analysis and machine learning algorithms to predict future occupancy rates of hotels. This prediction could help hotel managers formulate room prices, promotion strategies, and management strategies to meet market demand and improve hotel operating efficiency. Hotel occupancy forecast usually uses historical data, market trends, and other information to conduct model training and forecast. Prediction algorithms include time series analysis, regression analysis, neural network, random forest, etc. These algorithms could predict future occupancy rates by learning and predicting historical data while evaluating the impact of different factors on occupancy rates. Hotel managers can also use online prediction tools to predict occupancy rates. These tools usually use real-time data and market trends to make predictions and provide suggestions and decision support based on the prediction results. However, one of the worst impacts of the COVID-19 on the hotel industry, the hotel passenger flow has declined significantly. Due to the uncertainty of 596 K. Kozlovskis et al. Application of machine learning algorithms to predict hotel occupancy mobility control policies, travel is sometimes allowed but randomly controlled. Many tourists whose mobility has been restricted for a long time and who start planning to travel suddenly cancel their plans due to the lockdown of their residential areas or tourist destinations. The hotel customers flow significantly decreases and seriously impacts occupancy rates and profits. It leads to increased hotel operating costs, adjustments to hotel marketing strategies, hotel formats, and service models, and challenges the accuracy of hotel occupancy forecast models. This paper aims to apply several machine learning algorithms to predict high frequent daily hotel occupancy at a Chinese hotel. Five machine learning algorithms (bagged CART, bagged MARS, XGBoost, random forest, and SVM) were used to achieve the research purpose. Usually, these methods are intensively used for solving the tasks of classification. However, in this paper, these methods were used unusually for solving the regression tasks (all these algorithms can be applied for classification as for regression). In addition, all five machine learning tools were compared with a benchmark represented by a traditional econometric model based on the ARDL methodology. Also, the following hypotheses were checked: 1. The machine learning tools are more effective than traditional methods of forecasting (ARDL model) and should show higher forecasting power. 2. The Chinese Baidu search engine index and its components can be used for predicting hotel occupancy. The paper is organized in the following way. The actualization of the research conducted is given in the introduction part. The “Bibliographic mapping” provides the results of the network visualization based on the bibliometric data obtained from the Web of Science database using two searching keywords, “machine learning” and “hotel occupancy”. The next chapter, “Description of variables used in the research”, explains the variables that significantly impacted tourism demand. A description of the machine learning algorithms used follows this chapter. The measures of model accuracy allow us to compare the models between themselves as well as to assess their forecasting power. It is shown in chapter four. The presentation of the research results is shown in chapter five. The conclusion puts forward the main discoveries related to machine learning algorithms’ forecasting power (accuracy) and the results of testing hypotheses. 1. Bibliographic mapping Figure 1 shows the results of the network visualization based on the bibliometric data obtained from the Web of Science database using two searching keywords, “machine learning” and “hotel occupancy”. In the network visualization, all items related to two keywords in the scientific papers are represented by words (labels) with the highest frequency matched in the articles. The font size of the label and the square of the circle of any item is determined by the item’s weight in the total number of found words. The higher the frequency (weight) of an item, the larger the font size of the label and the square covered by the circle of the item. The different colors of items describe clusters to which the corresponding item belongs. Colorful lines represent the links between the items. In addition, the distance between two items approximately indicates the relatedness of the items, i.e., the closer are two items, the stronger their relatedness is (van Eck & Waltman, 2023). Journal of Business Economics and Management, 2023, 24(3): 594–613 597 Figure 1. Network visualization The results shown in Figure 1 can be analyzed by clusters highlighted in different colors: 1. Algorithms. The most frequent words are random forest, deep learning, neural networks, and support vector machine. All these words show the main trends in implementing the corresponding machine learning methods to predict hotel occupancy. 2. Fields of forecasting. There are four main words associated with forecasting: hotel occupancy, demand, accuracy, and forecasting itself. 3. A cluster of words “hotel”, “algorithms”, “model”, “arrivals”, “timeseries”, “forecasting tourism demand” can be considered as evidence of the usage of different algorithms and models based on time series of data to predict hotel occupancy or demand. 4. The last green cluster links occupancy, revenue management, tourism, big data, and genetic algorithm. Such a relationship between the words can be decoded as the usage of big data in tourism, particularly in occupancy, leads to more effective revenue management, and some genetic algorithms (probably as a class of optimizers) are used in this direction. Bibliographic mapping through network visualization discovered that machine learning algorithms such as random forest, support vector machine, neural network (recently defined as deep learning algorithms) are widely used in hotel business, including forecasting of hotel occupancy. Following the trend, similar methods were used in this research. 598 K. Kozlovskis et al. Application of machine learning algorithms to predict hotel occupancy 2. Description of variables used in the research Analyzing scientific literature, Lim (1997) showed that over 1961–1994 a various number of variables had a significant impact on tourism demand, such as income, tourism prices, relative prices, transportation costs, exchange rates, sociodemographic characteristics, ethnic or immigration factors, destination marketing, tourism attractiveness, and climate. Kim (2010) expanded the list of variables by discovering the significance of traveler data from luxury hotels in Seoul, South Korea. Yang etal. (2014) conducted one of the most exciting research, showing how Baidu search data allowed us to predict the passenger flow received by tourist attractions in Hainan Province, China. This paper used five main variables and their derivatives to predict hotel occupancy from July 1, 2017, to Nov 30, 2021, in a daily format. Table 1 shows the description of the variables. Table 1. A list of five main variables #Variable Description 1Hotel occupancy This is the target of interest. The variable is used as a dependent variable in the models considered in this paper. 2Weekend It is a dummy variable: 1 if Saturday or Sunday and 0, otherwise. 3 Temperature It represents the weather conditions by the average temperature in the region. The values are obtained from tianqihoubao.com. 4Public holidays It is a dummy variable: 1 if national holiday and 0, otherwise. The information on the public holidays formulated by the General Office of the National People’s Congress (NPC) over the study period was obtained from www.gov.cn. 5Baidu search index and 5 search keywords Consisted of the Baidu index itself and five components: “Guiyang tourism”, “Guiyang tourism”, “Guiyang food”, “Guiyang air ticket”, “Guiyang tourist attractions”, “Guiyang hotels”. These keywords can reflect tourists’ travel motives and needs and support conducting subsequent research. In addition to the main five variables, the following derivatives were added: – differenced variables of temperature, Baidu search index, as well as differenced values of five Baidu search index components; – lagged variables of temperature, Baidu search index, and its five components. The lag is from 1 to 7. Differenced and lagged variables were added to the set of variables based on the results of the autocorrelation function analysis (see Figures 2–9). Except for hotel occupancy and Guiyang air ticket keyword index value, all other variables show a clear presence of a trend by decaying the ACF values slowly. Based on the wave structure of the ACF of hotel occupancy, it can be concluded that the variable contains a seasonal component (maximal peaks are repeated each seventh day) (see Figure 2). The behavior of Guiyang air ticket keyword index values is similar to hotel occupancy but does not present a clear view of seasonality (see Figure 4). Journal of Business Economics and Management, 2023, 24(3): 594–613 599 Figure 2. Hotel occupancy Figure 3. Temperature Figure 4. Guiyang air ticket Figure 5. Guiyang Hotel Figure 6. Guiyang tourism Figure 7. Guiyang attractions 600 K. Kozlovskis et al. Application of machine learning algorithms to predict hotel occupancy Figure 8. Guiyang cuisine Figure 9. Baidu index A seasonal component at lag seven also offered to create lagged variables. It should be noted that the necessity to have differenced and lagged variables is not required by tools from the field of machine learning. Nevertheless, an enhanced set of variables can lead to better results. 3. Methods and datasets 3.1. Machine learning algorithms In this research, the following machine learning algorithms were used: 1. Bagged CART; 2. Bagged MARS; 3. XGBoost; 4. Random Forest; 5. SVM. Bagging or bootstrap aggregation is a specific methodology for reducing the forecasting error of learning algorithms. Breiman presented empirical evidence that bagging can reduce prediction error (Buja & Stuetzle, 2006). In general, bagging is realized in several steps: 1. Creating bootstrap samples from the training sample; 2. Applying the corresponding learning algorithm to each bootstrap sample; 3. Predicting by aggregating (usually averaging) the predicted values for test observations. It was proved that bagging is highly effective for CARTs (classification and regression trees) (Breiman, 1984). Bagged CART was realized using the bagging function from the ipred library of R. Bagged MARS is multivariate adaptive regression splines with bagging and is usually used for solving complex non-linear regression problems. As Friedman wrote in his article, “the Journal of Business Economics and Management, 2023, 24(3): 594–613 601 model takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and knot locations) are automatically determined by the data” (Friedman, 1991). Bagged MARS was realized using the earth function from the earth library of R. Extreme gradient boosting package called xgboost is an efficient and scalable implementation of gradient boosting framework referencing Friedman (2001) and Friedman etal. (2000). This package can solve as classification tasks as regression ones (Chen & He, 2023). XGBoost was realized using the xgboost function from the xgboost library of R. Random forest is a supervised machine learning algorithm that can also be used for solving regression tasks based on a group of decision tree models (Afriyie etal., 2023). In practice, random forest is characterized by accurate predictions, estimation of variables’ importance, etc. (Prajwala, 2015) Random forest was realized using the randomForest function from the randomForest library of R. Support vector machine, or support vector regression, is the adapted form of SVM when the dependent variable is numerical rather than categorical. The basic principle of SVR is to map the indistinguishable sample data from low dimension to high dimension, in which the indistinguishable sample data become distinguishable using the kernel function. Then, the SVR establishes a decision function based on the theory of structural risk minimization for regression analysis on distinguishable sample data (Sun etal., 2023). SVM was realized using the svm function from the e1071 library of R. The period of research covers 1,612 observations from Jul 1, 2017, to Nov 30, 2021. The whole period is divided into three parts called “before”, “during”, and “after” regarding the situation with COVID-19 in China. The periods of “during” and “after” COVID-19 are defined with the 07/2020 announcement by the Guizhou Provincial Tourism and Culture Department of the full resumption of cross-province (region and city) group travel in Guizhou as the time division point. All models were trained on the first 80% of data (724 observations) in the before-COVID- 19-time-period. After that, all models were tested on the other three sets (see Figure 10). Figure 10. Hotel occupancy over Jul 1, 2017– Nov 30, 2021 608 K. Kozlovskis et al. Application of machine learning algorithms to predict hotel occupancy Table 10 displays the TOP-10 of variable importance. The values of importance were extracted by the importance function from the randomForest library. Table 10. TOP-10 of variable importance (Random Forest) #Variable Importance 1 Guiyang.Hotel_1 4.11 2 Guiyang.attractions 3.90 3 Guiyang.cuisine_1 3.81 4 Baidu.index_1 3.75 5 Baidu.index 3.59 6 Baidu.index_2 3.51 7 Guiyang.cuisine 3.48 8 Guiyang.cuisine_5 3.39 9 Temperature_1 3.35 10 Guiyang.Hotel 3.17 Summarizing the results produced by the random forest model, it can be concluded that the model cannot predict hotel occupancy appropriately despite significant results obtained from the training data. All R2 under 50% were considered insufficient. 5.5. SVM Implementing a support vector machine technique on the training data, the coefficient of determination (R2) was sufficiently high (0.81). Nevertheless, extending the model on the testing data in each separate time period, these metrics dramatically decreased up to zero (see Table 11), signalizing the inability of the model to forecast hotel occupancy at an appropriate level. In the next two periods, the determination coefficient remained under 50%. Table 11. Assessment of model accuracy (SVM) Period Before (80%) Before (20%) During After Forecast bias –225 –4,429 4,470 –8,093 R20.81 0.01 0.36 0.48 Figure 18. MSR vs. number of trees 0 10 20 30 40 50 60 800 900 1000 1100 1200 1300 1400 10 20 30 40 50 60 70 80 90 100 % MSR number of trees MSR (mean squared residuals)Var explained Journal of Business Economics and Management, 2023, 24(3): 594–613 609 Table 12 displays the TOP-10 of variable importance. The values of importance were calculated by the Importance function from the rminer library. Table 12. TOP-10 of variable importance (SVM) #Variable Importance 1 dGuiyang.attractions 0.03 2 Guiyang.cuisine_6 0.02 3 Guiyang.cuisine_5 0.02 4Weekend 0.02 5 Guiyang.cuisine_7 0.02 6 Guiyang.cuisine_4 0.02 7 dGuiyang.Hotel 0.02 8 dTemperature 0.02 9 Guiyang.tourism_6 0.02 10 Guiyang.tourism_5 0.02 5.6. Benchmark The following results were obtained by applying the benchmark model to different time periods (see Table 13). In the case of forecast bias, the picture is relatively typical compared with other models: the model tends to underestimate in all time periods except the during- COVID-19 time period. Table 13. Assessment of model accuracy (benchmark) Period Before (80%) Before (20%) During After Forecast bias –360 –842 1,227 –615 R20.69 0.53 0.84 0.76 In the context of the coefficient of determination, the model shows relatively stable results. Based on the training data, the model was able to explain 69% of the initial variance. On the left 20% of the before-COVID-19 time period, the model showed 53% that can be considered appropriate. Notably, in the during-COVID-19 time period the model improved its forecasting power up to R2 = 84%. After the coefficient fell to 76%. Conclusions Considering all results obtained during the research, it can be concluded that: 1. The behavior of all models, including the benchmark, was quite typical: on the testing data the models showed negative bias signalizing the underestimation of hotel occupancy. Mostly all models had forecast bias equaled approximately 300 (except XGBoost where forecast bias = –806). The feature of underestimation remained on 610 K. Kozlovskis et al. Application of machine learning algorithms to predict hotel occupancy the testing data, showing a significant difference between actual and forecasted data (forecast bias was expressed in negative thousands). Nevertheless, in the during- COVID-19 time period overestimation appeared. This effect can be explained by the significant decrease in hotel occupancy caused by external factors (sufficient restrictions and limitations realized by the Chinese government during the COVID-19 pandemic). When the situation with COVID-19 stabilized, the models again showed more serious negative forecast bias. Thus, bagged CART, bagged MARS, random forest, XGBoost, and SVM tend to underestimate hotel occupancy in a daily format. The benchmark model (ARDL model) increased forecast bias insignificantly compared to other models. 2. The forecasting power (accuracy) of the machine learning algorithms used in this research can be estimated as weak because all coefficients of determination are less than 50% on the testing data (left 20% of the before-COVID-19 time period, during- and after-) except the bagged CART model that could explained 53%, 54%, and 68% of initial variance, respectively. The bagged CART model was the only one that could ensure the R2 coefficient greater than 50%. However, the benchmark model based on the ARDL model class showed better results: 53%, 84%, and 76%, respectively. 3. In the context of variable importance, it was found that the Baidu search index and its components related to hotel booking and visits to the corresponding city can be used to build a model to predict hotel occupancy. Summarizing the frequencies of how often the corresponding variable was used the TOP-10 of them looks as follows: – Guiyang.cuisine – Baidu.index – Guiyang.attractions – Guiyang.air.ticket – Guiyang.Hotel – Guiyang.tourism – Temperature – dGuiyang.Hotel – dTemperature – dGuiyang.cuisine 4. The weak forecasting power of the models (excluding bagged CART) shows that the task of regression (not classification) is more typical for the machine learning algorithms used in this research. Nevertheless, at least one algorithm– bagged CART– was able to predict more or less adequately high frequent daily hotel occupancy. Summarizing all above, the following conclusions on two hypotheses can be done: 1. In this case, the machine learning tools used in this research did not show more effective results than a traditional model based on the ARDL methodology. Thus, the first hypothesis on the higher effectiveness of machine learning tools compared to the traditional forecasting model based on the ARDL class of models is rejected. 2. As the variables’ importance showed, the Chinese Baidu search engine index and its components were used in the machine learning models. Thus, the second hypothesis on the possible usage of the Baidu search engines values and its components is accepted. Journal of Business Economics and Management, 2023, 24(3): 594–613 611 Despite the weak results of the application of five machine learning algorithms for the task of regression the forecasting power could be improved due to several reasons: 1. All models have a list of different hyperparameters that could be tuned more specifically for the regression task. 2. 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